






"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(a_	):
	__magic_name__       =							['''input_features''', '''attention_mask''']





	def __init__( self   :					Dict      ,      lowerCAmelCase_   :					Optional[Any]=80      ,      lowerCAmelCase_   :					Tuple=16_000      ,      lowerCAmelCase_   :					Dict=80      ,      lowerCAmelCase_   :					List[str]=0.0      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Tuple=True      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     List[str]:
				super().__init__(feature_size=UpperCamelCase_      ,      sampling_rate=UpperCamelCase_      ,      padding_value=UpperCamelCase_      ,      **UpperCamelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   num_mel_bins
				UpperCAmelCase_	:					int     	=   do_ceptral_normalize
				UpperCAmelCase_	:					Dict     	=   normalize_means
				UpperCAmelCase_	:					Union[str, Any]     	=   normalize_vars
				UpperCAmelCase_	:					Optional[int]     	=   True





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					np.ndarray      ,      )		->     Optional[int]:
				UpperCAmelCase_	:					Optional[int]     	=   waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
				UpperCAmelCase_	:					int     	=   torch.from_numpy(UpperCamelCase_		).unsqueeze(0		)
				UpperCAmelCase_	:					List[str]     	=   ta_kaldi.fbank(UpperCamelCase_      ,      num_mel_bins=self.num_mel_bins      ,      sample_frequency=self.sampling_rate		)
				return features.numpy()





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Optional[bool] = True      ,      lowerCAmelCase_   :					Optional[bool] = True      ,      lowerCAmelCase_   :					float = 0.0      ,      )		->     List[Any]:

				if normalize_means:
							UpperCAmelCase_	:					Union[str, Any]     	=   x[:input_length].mean(axis=0		)
							UpperCAmelCase_	:					List[str]     	=   np.subtract(UpperCamelCase_      ,      UpperCamelCase_		)
				if normalize_vars:
							UpperCAmelCase_	:					Union[str, Any]     	=   x[:input_length].std(axis=0		)
							UpperCAmelCase_	:					Optional[int]     	=   np.divide(UpperCamelCase_      ,      UpperCamelCase_		)

				if input_length < x.shape[0]:
							UpperCAmelCase_	:					int     	=   padding_value

				# make sure array is in float32
				UpperCAmelCase_	:					str     	=   x.astype(np.floataa		)

				return x





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					List[np.ndarray]      ,      lowerCAmelCase_   :					Optional[np.ndarray] = None		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[str]     	=   attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [
				    self.utterance_cmvn(UpperCamelCase_      ,      UpperCamelCase_      ,      self.normalize_means      ,      self.normalize_vars      ,      self.padding_value		)
				    for x, n in zip(UpperCamelCase_      ,      UpperCamelCase_		)
				]





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      **lowerCAmelCase_   :					Union[str, Any]      ,      )		->     Optional[int]:

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
										    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
										    f""" {self.sampling_rate} and not {sampling_rate}."""		)
				else:
							logger.warning(
							    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
							    "Failing to do so can result in silent errors that might be hard to debug."		)

				UpperCAmelCase_	:					int     	=   isinstance(UpperCamelCase_      ,      np.ndarray		) and len(raw_speech.shape		) > 1
				if is_batched_numpy and len(raw_speech.shape		) > 2:
							raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
				UpperCAmelCase_	:					Any     	=   is_batched_numpy or (
				    isinstance(UpperCamelCase_      ,      (list, tuple)		) and (isinstance(raw_speech[0]      ,      (np.ndarray, tuple, list)		))
				)

				if is_batched:
							UpperCAmelCase_	:					int     	=   [np.asarray(UpperCamelCase_      ,      dtype=np.floataa		) for speech in raw_speech]
				elif not is_batched and not isinstance(UpperCamelCase_      ,      np.ndarray		):
							UpperCAmelCase_	:					Optional[Any]     	=   np.asarray(UpperCamelCase_      ,      dtype=np.floataa		)
				elif isinstance(UpperCamelCase_      ,      np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_	:					Tuple     	=   raw_speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
							UpperCAmelCase_	:					Tuple     	=   [raw_speech]

				# extract fbank features
				UpperCAmelCase_	:					Optional[Any]     	=   [self._extract_fbank_features(UpperCamelCase_		) for waveform in raw_speech]

				# convert into correct format for padding
				UpperCAmelCase_	:					Union[str, Any]     	=   BatchFeature({"input_features": features}		)

				UpperCAmelCase_	:					Optional[Any]     	=   self.pad(
				    UpperCamelCase_      ,      padding=UpperCamelCase_      ,      max_length=UpperCamelCase_      ,      truncation=UpperCamelCase_      ,      pad_to_multiple_of=UpperCamelCase_      ,      return_attention_mask=UpperCamelCase_      ,      **UpperCamelCase_      ,      )

				# make sure list is in array format
				UpperCAmelCase_	:					Optional[int]     	=   padded_inputs.get("input_features"		)
				if isinstance(input_features[0]      ,      UpperCamelCase_		):
							UpperCAmelCase_	:					Optional[Any]     	=   [np.asarray(UpperCamelCase_      ,      dtype=np.floataa		) for feature in input_features]

				UpperCAmelCase_	:					Dict     	=   padded_inputs.get("attention_mask"		)
				if attention_mask is not None:
							UpperCAmelCase_	:					Dict     	=   [np.asarray(UpperCamelCase_      ,      dtype=np.intaa		) for array in attention_mask]

				# Utterance-level cepstral mean and variance normalization
				if self.do_ceptral_normalize:
							UpperCAmelCase_	:					Optional[Any]     	=   (
							    np.array(UpperCamelCase_      ,      dtype=np.intaa		)
							    if self._get_padding_strategies(UpperCamelCase_      ,      max_length=UpperCamelCase_		) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							UpperCAmelCase_	:					List[str]     	=   self.normalize(
							    padded_inputs["input_features"]      ,      attention_mask=UpperCamelCase_		)

				if return_tensors is not None:
							UpperCAmelCase_	:					Tuple     	=   padded_inputs.convert_to_tensors(UpperCamelCase_		)

				return padded_inputs











"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp